Load the packages

library(Seurat)
library(data.table)
library(NMF)
library(rsvd)
library(Rtsne)
library(ggplot2)
library(cowplot)
library(sva)
library(igraph)
library(cccd)
library(KernSmooth)
library(beeswarm)
library(stringr)
library(formatR)
source("../tools.R")
library(ggthemes)
library(DESeq2)

The function will be used in the follow

Analysis based on cell size

According to the previous analysis on sample group,remove the group hc001 and cell size 2um ### Read data ### Data QA

human.only.pro <- Load_data(data_dir = "../data/human.txt")
important.genes <- c("ITGB4", "ABCB5", "KRT19", "ACTB", "KRT12", "KRT5", "GAPDH", 
    "KRT3", "PAX6", "WNT7A", "KRT14", "TP63", "KRT10")
human.only.pro <- human.only.pro[, colnames(human.only.pro)[unlist(lapply(colnames(human.only.pro), 
    function(x) return(str_split(x, "_")[[1]][2]))) %in% c("10um", "20um", "6um")]]
human.only.pro <- human.only.pro[, colnames(human.only.pro)[!unlist(lapply(colnames(human.only.pro), 
    function(x) return(str_split(x, "_")[[1]][1]))) %in% c("hc001", "shoutiao")]]
# human.only.pro<-Simplify_Select(human.only.pro)
human.all.DESeq <- DESeq_SeuratObj(X = human.only.pro, DESq = FALSE, min.cells = 10, 
    min.genes = 2)
## [1] "Scaling data matrix"
## 
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human.imp.lognorm <- data.frame(FetchData(human.all.DESeq, vars.all = important.genes[important.genes %in% 
    rownames(human.all.DESeq@raw.data)]))

Split the data according to whether the gene ITGB4 is Negative or negative

library(ggplot2)
library(reshape2)

ITGB4 <- as.numeric(human.imp.lognorm[, "ITGB4"])
Positive.idx <- which(ITGB4 > 0)
Negative.idx <- which(ITGB4 == 0)
Positive.data <- human.imp.lognorm[Positive.idx, , drop = FALSE]
Negative.data <- human.imp.lognorm[Negative.idx, , drop = FALSE]
Positive.data <- Positive.data[, -1]  # remove ITGB4
Negative.data <- Negative.data[, -1]

Figure Explore.1

# Positive.t<-data.frame(as.matrix(LogNormalize(Positive.data,display.progress
# = FALSE)))
# Negative.t<-data.frame(as.matrix(LogNormalize(Negative.data,display.progress
# = FALSE))) Positive.t<-data.frame(t(Positive.t[important.genes,]))
# Negative.t<-data.frame(t(Negative.t[important.genes,]))
plot.data <- rbind(Positive.data, Negative.data)
plot.data$condition <- rep(c("ITGB4+", "ITGB4-"), times = c(dim(Positive.data)[1], 
    dim(Negative.data)[1]))
cell.size <- c(unlist(lapply(rownames(Positive.data), function(x) return(str_split(x, 
    "_")[[1]][2]))), unlist(lapply(rownames(Negative.data), function(x) return(str_split(x, 
    "_")[[1]][2]))))


plot.data$cell.size <- cell.size
X <- melt(plot.data)

Melt the data

Violin

# p<-ggplot(data = X,aes(y=value,x=condition,fill=cell.size))
# p+geom_violin(trim = FALSE,scale =
# 'width')+facet_wrap(~variable+condition)+
# geom_jitter()+guides(fill=guide_legend(title='Cell Size'))

for (var in as.character(unique(X$variable))) {
    p <- ggplot(data = X[X$variable == var, ], aes(y = value, x = condition, 
        fill = cell.size))
    print(p + geom_violin(trim = FALSE, scale = "width") + geom_jitter() + guides(fill = guide_legend(title = "Cell Size")) + 
        ggtitle(label = var))
}

Boxplot

# p<-ggplot(data = X,aes(y=value,x=condition,fill=cell.size))
# p+geom_boxplot()+guides(fill=guide_legend(title='Cell
# Size'))+facet_wrap(~variable+condition)
for (var in as.character(unique(X$variable))) {
    p <- ggplot(data = X[X$variable == var, ], aes(y = value, x = condition, 
        fill = cell.size))
    print(p + geom_boxplot() + guides(fill = guide_legend(title = "Cell Size")) + 
        ggtitle(label = var))
}

Density,histogram

ggplot(data = X, aes(x = value, fill = variable)) + geom_density(kernel = "gaussian") + 
    scale_x_log10() + facet_wrap(~condition + cell.size)

ggplot(data = X, aes(x = value, fill = variable)) + geom_density(kernel = "gaussian", 
    position = "stack") + scale_x_log10() + facet_wrap(~condition + cell.size)

ggplot(data = X, aes(x = value, fill = variable)) + geom_histogram() + scale_x_log10() + 
    facet_wrap(~condition + cell.size)

ITGB4 <- as.integer(human.only.pro["ITGB4", ])
Positive.idx <- which(ITGB4 > 0)
Negative.idx <- which(ITGB4 == 0)
Positive.data <- human.only.pro[, Positive.idx, drop = FALSE]
Negative.data <- human.only.pro[, Negative.idx, drop = FALSE]

Create Seurat object and not caculate DESeq

Positive.pbmc <- DESeq_SeuratObj(X = Positive.data, min.cells = 10, min.genes = 2)
## [1] "Scaling data matrix"
## 
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Positive.sample.group <- unlist(lapply(Positive.pbmc@cell.names, function(x) return(str_split(x, 
    "_")[[1]][1])))
Positive.sample.cellsize <- unlist(lapply(Positive.pbmc@cell.names, function(x) return(str_split(x, 
    "_")[[1]][2])))

Positive.pbmc <- SetIdent(Positive.pbmc, cells.use = Positive.pbmc@cell.names, 
    ident.use = Positive.sample.cellsize)
Negative.pbmc <- DESeq_SeuratObj(X = Negative.data, min.cells = 10, min.genes = 2)
## [1] "Scaling data matrix"
## 
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  |=================================================================| 100%

Negative.sample.group <- unlist(lapply(Negative.pbmc@cell.names, function(x) return(str_split(x, 
    "_")[[1]][1])))
Negative.sample.cellsize <- unlist(lapply(Negative.pbmc@cell.names, function(x) return(str_split(x, 
    "_")[[1]][2])))

Negative.pbmc <- SetIdent(Negative.pbmc, cells.use = Negative.pbmc@cell.names, 
    ident.use = Negative.sample.cellsize)

Accordind to the Dispersion vs Avearge expression of Positive and Negative data on ITGB4,they tell us that the although they have similar shape and trend,dispersion of Positive data is more significant than Negative in some genes.

Step 1: analysis on Positive data

Figure Explore.2

First,use the plot,eg. Barplot,Violin…,we can explore some message from sample

Group_Bar(Positive.pbmc@raw.data, group = Positive.sample.group)

Group_Bar(Positive.pbmc@raw.data, group = Positive.sample.cellsize)

VlnPlot(Positive.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Positive.pbmc@raw.data)], y.lab.rot = 90)  # Violinn plot of gene ITGB in all sample

Dimensionality reduction

PCA and tSNE

Here,do the dimensionality reduction using the PCA, tSNE method

It will take a long time to caculate significant pcs.So,here we use the default value

Positive.pbmc <- PCA.TSNE(object = Positive.pbmc, pcs.compute = FALSE, num.pcs = 28)

After the PCA and tSNE,try plot: Featureplot of ITGB4,four var.genes,PCA plot,tSNE plot…

FeaturePlot(object = Positive.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Positive.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "pca")  # ITGB4 gene in part dataset

FeaturePlot(object = Positive.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Positive.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "tsne")  # ITGB4 gene in part dataset

DimPlot(Positive.pbmc, reduction.use = "tsne", pt.size = 4)  #  grour by sample

DimPlot(Positive.pbmc, reduction.use = "pca", pt.size = 4)  #  grour by sample

DimHeatmap(Positive.pbmc, reduction.type = "pca", check.plot = FALSE)

The Faetureplot of ITGB4, ABCB5, KRT19, ACTB, KRT12, KRT5, GAPDH, KRT3, PAX6, WNT7A, KRT14, TP63, KRT10based on PCA shows that,they only has high expression level in few samples,and expresss lowly in most sample.It means that may be these important genes express differently across sample.The plot also tell us the gene KRT5,GAPDH,PAXX6,KRT14 have more higher expression level than the other important genes.It is consistent with the result of violin plot. About the heatmap,we only show the gene ITGB4 And the FeatureHeatmap and Heamap also comfirm this phenomeno.We try the other four variable genes,which has the similar result as gene ITGB4 But the tSNE and * PCA * plot show that, the sample can not be split apparently.The result may be is not good based on the PCA and tSNE method.

Differential expression

Next,we will have analysis on gene differential expression.Find maker genes across sample.We use the method: **wilcox test**
# Finds markers (differentially expressed genes) for each of the identity
# classes in a dataset
Positive.markers <- FindAllMarkers(Positive.pbmc, test.use = "bimod", print.bar = FALSE)
head(Positive.markers)
##                      p_val  avg_logFC pct.1 pct.2    p_val_adj cluster
## RP11-217O12.1 6.454120e-95  2.3017192 1.000 1.000 1.014071e-90     6um
## AC009501.4    9.144325e-59  2.1192914 0.946 0.322 1.436756e-54     6um
## FLNA          7.333467e-27  1.1575461 0.811 0.269 1.152234e-22     6um
## MT-CO2        9.767696e-25 -1.7782097 1.000 1.000 1.534700e-20     6um
## AHNAK         1.157653e-24  1.5878616 0.973 0.902 1.818904e-20     6um
## RP11-529H20.3 1.863462e-23 -0.3520236 0.054 0.871 2.927871e-19     6um
##                        gene
## RP11-217O12.1 RP11-217O12.1
## AC009501.4       AC009501.4
## FLNA                   FLNA
## MT-CO2               MT-CO2
## AHNAK                 AHNAK
## RP11-529H20.3 RP11-529H20.3

We check whether the important genes are still in the marker genes we found from the DESeq analysis. the genes:ITGB4, KRT19, ACTB, GAPDH, KRT10 are still in the marker genes.

Bar plot of gene’s p.val

Positive.heatmap <- Heatmap_fun(genes = important.genes[important.genes %in% 
    rownames(Positive.pbmc@raw.data)], tpm.data = Positive.pbmc@scale.data, 
    condition = unique(as.character(Positive.pbmc@ident)), all.condition = as.character(Positive.pbmc@ident))
## There ara 3 conditions
## Whether creat data accurate 0
NMF::aheatmap(Positive.heatmap[[2]], Rowv = NA, Colv = NA, annCol = Positive.heatmap[[1]], 
    scale = "none")

We have find all marker genes across sample,there are 937 significant genes(adjust p-value <0.05) in all marker genes.

Next,Spectral k-means clustering on single cells based on PCA

Positive.pbmc <- KClustDimension(Positive.pbmc, reduction.use = "pca", k.use = 3)
clusters.pca <- Positive.pbmc@meta.data$kdimension.ident
DimPlot(Positive.pbmc, pt.size = 4, group.by = "kdimension.ident")

Spectral k-means clustering on single cells based on tSNE

Positive.pbmc <- KClustDimension(Positive.pbmc, reduction.use = "tsne", k.use = 3)
clusters.tsne <- Positive.pbmc@meta.data$kdimension.ident
DimPlot(Positive.pbmc, pt.size = 4, group.by = "kdimension.ident", reduction.use = "tsne")

Step 2: analysis on Negative data

Figure Explore

First,use the plot,eg. Barplot,Violin…,we can explore some message from sample

Group_Bar(Negative.pbmc@raw.data, group = Negative.sample.group)

Group_Bar(Negative.pbmc@raw.data, group = Negative.sample.cellsize)

VlnPlot(Negative.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Negative.pbmc@raw.data)], y.lab.rot = 90)  # Violinn plot of gene ITGB in all sample

Dimensionality reduction

PCA and tSNE

Here,do the dimensionality reduction using the PCA, tSNE method

It will take a long time to caculate significant pcs.So,here we use the default value

Negative.pbmc <- PCA.TSNE(object = Negative.pbmc, pcs.compute = FALSE, num.pcs = 28)

After the PCA and tSNE,try plot: Featureplot of ITGB4,four var.genes,PCA plot,tSNE plot…

FeaturePlot(object = Negative.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Negative.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "pca")

FeaturePlot(object = Negative.pbmc, features.plot = important.genes[important.genes %in% 
    rownames(Negative.pbmc@raw.data)], pt.size = 1, no.legend = FALSE, reduction.use = "tsne")

DimPlot(Negative.pbmc, reduction.use = "tsne", pt.size = 4)  #  grour by sample

DimPlot(Negative.pbmc, reduction.use = "pca", pt.size = 4)  #  grour by sample

DimHeatmap(Negative.pbmc, reduction.type = "pca", check.plot = FALSE)

The Faetureplot of ITGB4, ABCB5, KRT19, ACTB, KRT12, KRT5, GAPDH, KRT3, PAX6, WNT7A, KRT14, TP63, KRT10based on PCA shows that,they only has high expression level in few samples,and expresss lowly in most sample.It means that may be these important genes express differently across sample.The plot also tell us the gene KRT5,GAPDH,PAXX6,KRT14 have more higher expression level than the other important genes.It is consistent with the result of violin plot. About the heatmap,we only show the gene ITGB4 And the FeatureHeatmap and Heamap also comfirm this phenomeno.We try the other four variable genes,which has the similar result as gene ITGB4 But the tSNE and * PCA * plot show that, the sample can not be split apparently.The result may be is not good based on the PCA and tSNE method.

Differential expression

Next,we will have analysis on gene differential expression.Find maker genes across sample.We use the method: **wilcox test**
# Finds markers (differentially expressed genes) for each of the identity
# classes in a dataset
Negative.markers <- FindAllMarkers(Negative.pbmc, test.use = "bimod", print.bar = FALSE)
head(Negative.markers)
##                      p_val  avg_logFC pct.1 pct.2    p_val_adj cluster
## RP11-217O12.1 1.169891e-91  3.0194034 0.989 0.949 1.636561e-87     6um
## AC009501.4    5.001195e-55  3.3911245 0.759 0.093 6.996172e-51     6um
## MT-CO2        7.052448e-42 -2.2905234 0.770 0.968 9.865670e-38     6um
## MYL6P1        1.498695e-39  0.4207640 0.034 0.291 2.096525e-35     6um
## RPS4XP8       1.829150e-38  0.2555633 0.034 0.145 2.558798e-34     6um
## CYP24A1       9.961353e-36  2.7452522 0.586 0.053 1.393494e-31     6um
##                        gene
## RP11-217O12.1 RP11-217O12.1
## AC009501.4       AC009501.4
## MT-CO2               MT-CO2
## MYL6P1               MYL6P1
## RPS4XP8             RPS4XP8
## CYP24A1             CYP24A1

We check whether the important genes are still in the marker genes we found from the DESeq analysis. the genes:KRT19, ACTB, KRT5, KRT3, PAX6, KRT14 are still in the marker genes.

Bar plot of gene’s p.val

Negative.heatmap <- Heatmap_fun(genes = important.genes[important.genes %in% 
    rownames(Negative.pbmc@raw.data)], tpm.data = Negative.pbmc@scale.data, 
    condition = unique(as.character(Negative.pbmc@ident)), all.condition = as.character(Negative.pbmc@ident))
## There ara 3 conditions
## Whether creat data accurate 0
NMF::aheatmap(Negative.heatmap[[2]], Rowv = NA, Colv = NA, annCol = Negative.heatmap[[1]], 
    scale = "none")

We have find all marker genes across sample,there are 824 significant genes(adjust p-value <0.05) in all marker genes.

Next,Spectral k-means clustering on single cells based on PCA

Negative.pbmc <- KClustDimension(Negative.pbmc, reduction.use = "pca", k.use = 3)
clusters.pca <- Negative.pbmc@meta.data$kdimension.ident
DimPlot(Negative.pbmc, pt.size = 4, group.by = "kdimension.ident")

Spectral k-means clustering on single cells based on tSNE

Negative.pbmc <- KClustDimension(Negative.pbmc, reduction.use = "tsne", k.use = 3)
clusters.tsne <- Negative.pbmc@meta.data$kdimension.ident
DimPlot(Negative.pbmc, pt.size = 4, group.by = "kdimension.ident", reduction.use = "tsne")